"""simple docstring"""



import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler






class  snake_case__      (       snake_case_       ):


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		):
        super().__init__()
        __a       =    value_function
        __a       =    unet
        __a       =    scheduler
        __a       =    env
        __a       =    env.get_dataset()
        __a       =    {}
        for key in self.data.keys():
               try:
                      __a       =    self.data[key].mean()
               except:  # noqa: E722
                      pass
        __a       =    {}
        for key in self.data.keys():
               try:
                      __a       =    self.data[key].std()
               except:  # noqa: E722
                      pass
        __a       =    env.observation_space.shape[0]
        __a       =    env.action_space.shape[0]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        return (x_in - self.means[key]) / self.stds[key]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase						):
        return x_in * self.stds[key] + self.means[key]


 def 						a__   ( self    ,		lowerCamelCase						):
        if type(lowerCamelCase						) is dict:
               return {k: self.to_torch(lowerCamelCase						) for k, v in x_in.items()}
        elif torch.is_tensor(lowerCamelCase						):
               return x_in.to(self.unet.device						)
        return torch.tensor(lowerCamelCase    ,		device=self.unet.device						)


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        for key, val in cond.items():
               __a       =    val.clone()
        return x_in


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						):
        __a       =    x.shape[0]
        __a       =    None
        for i in tqdm.tqdm(self.scheduler.timesteps						):
               # create batch of timesteps to pass into model
               __a       =    torch.full((batch_size,)    ,		lowerCamelCase    ,		device=self.unet.device    ,		dtype=torch.long						)
               for _ in range(lowerCamelCase						):
                      with torch.enable_grad():
                             x.requires_grad_()

                             # permute to match dimension for pre-trained models
                             __a       =    self.value_function(x.permute(0    ,		2    ,		1						)    ,		lowerCamelCase						).sample
                             __a       =    torch.autograd.grad([y.sum()]    ,		[x]						)[0]

                             __a       =    self.scheduler._get_variance(lowerCamelCase						)
                             __a       =    torch.exp(0.5 * posterior_variance						)
                             __a       =    model_std * grad

                      __a       =    0
                      __a       =    x.detach()
                      __a       =    x + scale * grad
                      __a       =    self.reset_xa(lowerCamelCase    ,		lowerCamelCase    ,		self.action_dim						)

               __a       =    self.unet(x.permute(0    ,		2    ,		1						)    ,		lowerCamelCase						).sample.permute(0    ,		2    ,		1						)

               # TODO: verify deprecation of this kwarg
               __a       =    self.scheduler.step(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		predict_epsilon=lowerCamelCase						)["prev_sample"]

               # apply conditions to the trajectory (set the initial state)
               __a       =    self.reset_xa(lowerCamelCase    ,		lowerCamelCase    ,		self.action_dim						)
               __a       =    self.to_torch(lowerCamelCase						)
        return x, y






 def __call__( self    ,		lowerCamelCase    ,		lowerCamelCase=64    ,		lowerCamelCase=32    ,		lowerCamelCase=2    ,		lowerCamelCase=0.1						):
        # normalize the observations and create  batch dimension
        __a       =    self.normalize(lowerCamelCase    ,		"observations"						)
        __a       =    obs[None].repeat(lowerCamelCase    ,		axis=0						)

        __a       =    {0: self.to_torch(lowerCamelCase						)}
        __a       =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

        # generate initial noise and apply our conditions (to make the trajectories start at current state)
        __a       =    randn_tensor(lowerCamelCase    ,		device=self.unet.device						)
        __a       =    self.reset_xa(lowerCamelCase    ,		lowerCamelCase    ,		self.action_dim						)
        __a       =    self.to_torch(lowerCamelCase						)

        # run the diffusion process
        __a ,   __a       =    self.run_diffusion(lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase						)

        # sort output trajectories by value
        __a       =    y.argsort(0    ,		descending=lowerCamelCase						).squeeze()
        __a       =    x[sorted_idx]
        __a       =    sorted_values[:, :, : self.action_dim]
        __a       =    actions.detach().cpu().numpy()
        __a       =    self.de_normalize(lowerCamelCase    ,		key="actions"						)

        # select the action with the highest value
        if y is not None:
               __a       =    0
        else:
               # if we didn't run value guiding, select a random action
               __a       =    np.random.randint(0    ,		lowerCamelCase						)

        __a       =    denorm_actions[selected_index, 0]
        return denorm_actions

"""simple docstring"""



import os



def 	_lowerCamelCase(	a = "matrix.txt"				):
       with open(os.path.join(os.path.dirname(a				)   ,    a				)				) as in_file:
              __a       =    in_file.read()

       __a       =    [[int(a				) for cell in row.split(","				)] for row in data.strip().splitlines()]
       __a       =    [[0 for cell in row] for row in grid]
       __a       =    len(grid[0]				)

       __a       =    [[0 for i in range(a				)] for j in range(a				)]
       __a       =    grid[0][0]
       for i in range(1   ,    a				):
              __a       =    grid[0][i] + dp[0][i - 1]
       for i in range(1   ,    a				):
              __a       =    grid[i][0] + dp[i - 1][0]

       for i in range(1   ,    a				):
              for j in range(1   ,    a				):
                     __a       =    grid[i][j] + min(dp[i - 1][j]   ,    dp[i][j - 1]				)

       return dp[-1][-1]


if __name__ == "__main__":
   print(F'''{solution() = }''')
